AI Engineer + Java

Tata Consultancy Services; Readington Township (NJ); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Comprehensive Medical Coverage

401K Plan

Training & Certification Reimbursement

Vacation & Holidays

Job summary

Tata Consultancy Services (TCS) is seeking a senior AI/legacy modernization lead in the United States to guide AI-augmented reverse and forward engineering for a mainframe to cloud-native stack.

You will architect multi-agent pipelines, supervise cross-functional teams, ensure adherence to architectural patterns, and drive migration validation with SMEs. The role emphasizes code quality, observability, and best practices in AI-assisted engineering.

Qualifications

• Bachelor of Computer Science or equivalent.
• Strong experience in AI agent design and modernization projects.
• Proven leadership of cross-functional teams and architecture reviews.

Responsibilities

• Design AI agent pipelines to ingest legacy artefacts and output structured domain models.
• Lead multi-agent workflows, ensure validation against live data and SME feedback.
• Oversee forward engineering outputs: service skeletons, contracts, schemas, and tests.

Skills

AI agent design

Leadership

Technical architecture

Code generation & testing

Cross-functional teamwork

Education

Bachelor of Computer Science

Tools

Claude Code CLI

Cursor

Gemini CLI

LangChain

LangGraph

AutoGen

CrewAI

Anthropic SDK

OpenAI API

Job description

Job Description

Must Have Technical/Functional Skills

The Modernisation Mission

Legacy Estates In Scope Typically Include

• Mainframe COBOL/CICS/IMS batch and online transaction processing
• Hierarchical and relational databases (IMS, DB2) with deeply embedded business logic
• Proprietary messaging middleware (IBM MQ) and brittle point-to-point integrations
• Legacy OO platforms (VisualAge Smalltalk, Tonel format) with no test coverage or documentation
• JCL/Assembler job streams woven into business-critical workflows

Your mission: deploy AI agent chains to extract, analyse, and understand this estate at depth — then drive forward engineering onto a modern Spring Boot / Java 21 / Angular / MongoDB cloud-native stack, with AI agents accelerating design, code generation, test authoring, and migration validation at every step.

Key Responsibilities

AI-Augmented Reverse Engineering

• Design and deploy custom AI agent pipelines that ingest legacy artefacts — COBOL programs, IMS DBDs/PSBs, DB2 schemas, JCL, Smalltalk Tonel sources — and produce structured outputs: business rule inventories, data-flow maps, domain entity models, and dependency graphs
• Build multi-agent chains that cross-reference extracted business logic against live transaction traces, test outputs, and production data patterns to validate completeness and surface hidden edge cases
• Use agents to auto-generate legacy comprehension artefacts: annotated COBOL walkthroughs, IMS segment relationship diagrams, CICS program call trees, and DB2-to-document data-model mappings
• Orchestrate agent workflows that identify dead code, duplicated logic, and tightly coupled components — producing prioritised decomposition candidates for the modernisation backlog
• Validate agent-extracted business rules against domain SMEs; build feedback loops that improve agent accuracy over successive extraction cycles

AI-Augmented Forward Engineering

• Design forward engineering agent chains that consume reverse-engineered domain models and produce: Spring Boot service skeletons, OpenAPI 3.1 contracts, MongoDB schema designs, Angular component scaffolds, and JUnit 5 test suites — all aligned to team coding standards
• Build agents that enforce architectural patterns during code generation: no business logic in adapters, domain models free of persistence concerns, API contracts decoupled from internal representations
• Deploy agents for migration validation — automatically comparing migrated service behaviour against legacy outputs across a curated test corpus, flagging behavioural divergence before human review
• Use AI to accelerate CI/CD pipeline authoring, infrastructure-as-code generation (Terraform, Helm), and runbook drafting — with engineers reviewing and owning the outputs, not rubber-stamping them
• Chain agents to continuously scan modernised code for legacy anti-patterns bleeding into new services, enforce non-functional requirements (observability hooks, circuit breakers, health endpoints), and flag design drift from approved blueprints

Custom Agent Design & Engineering

• Architect multi-agent systems using one or more agentic AI platforms and frameworks:
• Cl aude Code CLI (Anthropic) — agentic coding, slash commands, MCP tool integration, custom agent loops
• Cursor — AI-native IDE agent workflows, codebase-wide context, rule-based agent behaviour
• Gemini CLI (Google) — Gemini-powered agent pipelines with tool use and long-context reasoning
• LangChain / LangGraph — chain and graph-based agent orchestration, tool registries, state machines
• AutoGen / CrewAI — multi-agent conversation frameworks, role-based agent specialisation
• Anthropic Agent SDK / OpenAI Assistants API — programmatic agent construction with tool use, memory, and structured output
• Select the right orchestration pattern for each workstream: sequential chains, parallel fan-out, supervisor/worker, reflection loops, human-in-the-loop checkpoints
• Build domain-specific agent tools: legacy code readers, schema extractors, API contract validators, test harness runners, cloud cost estimators, IaC generators
• Design human-in-the-loop checkpoints: define what agents decide autonomously, what they flag for engineer review, and what requires architect sign-off
• Evaluate, benchmark, and improve agent chain quality: extraction completeness, forward-engineering accuracy, false-positive rates, and time-to-output

Solution Design & Technical Authority

• Own end-to-end solution design for modernisation workstreams — producing LLD documents, sequence diagrams, PlantUML/Mermaid data-model mappings, strangler-fig migration maps, and API surface designs
• Evaluate architectural trade-offs: lift-and-shift vs. re-platform vs. re-architect, agent-generated vs. hand-crafted, monolith decomposition sequencing — all documented as ADRs with explicit rationale
• Define integration patterns for hybrid-state environments: mainframe co-existence, MQ-to-event-streaming migration, dual-write data consistency, feature-flag-controlled cutovers
• Lead design reviews; drive alignment between AI workstream leads, legacy SMEs, domain engineers, and cloud platform teams

Technical Leadership & Team Development

• Lead a cross-functional team spanning backend, frontend, data migration, and AI/agent engineering
• Conduct structured code reviews across both hand-authored and agent-generated code — human review of AI output is non-negotiable; agents accelerate, engineers own
• Establish standards for agent-assisted development: what must be reviewed, what must be tested, how agent outputs are versioned and audited
• Mentor engineers on agentic AI patterns, prompt engineering for code tasks, and responsible use of AI-generated artefacts in production systems
• Coach engineers unfamiliar with legacy systems to read COBOL/IMS structures via agent-assisted comprehension tools you have built

Delivery Execution

• Break modernization epics into sprint-deliverable stories with measurable progress indicators: % business logic migrated, legacy endpoints retired, agent pipeline accuracy metrics
• Track and communicate migration coverage — human-readable progress dashboards built partly by agents, owned by you
• Identify and mitigate transition risks: agent hallucination in business rule extraction, data consistency during dual-write phases, performance parity of migrated services
• Own sprint-level commitments; surface blockers with proposed mitigations, not status updates

TCS Employee Benefits Summary

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
• Salary Range: $120,000 – 160,000 a year

Qualifications

• BACHELOR OF COMPUTER SCIENCE